Network Level Measures
Measure Value Row count 43.000 Column count 43.000 Link count 40.000 Density 0.022 Components of 1 node (isolates) 12 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 2 Reciprocity 0.053 Characteristic path length 3.098 Clustering coefficient 0.061 Network levels (diameter) 8.000 Network fragmentation 0.729 Krackhardt connectedness 0.271 Krackhardt efficiency 0.958 Krackhardt hierarchy 0.901 Krackhardt upperboundedness 0.931 Degree centralization 0.038 Betweenness centralization 0.051 Closeness centralization 0.011 Eigenvector centralization 0.476 Reciprocal (symmetric)? No (5% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.048 0.012 0.011 Total degree centrality [Unscaled] 0.000 8.000 1.953 1.928 In-degree centrality 0.000 0.060 0.012 0.013 In-degree centrality [Unscaled] 0.000 5.000 0.977 1.110 Out-degree centrality 0.000 0.060 0.012 0.013 Out-degree centrality [Unscaled] 0.000 5.000 0.977 1.131 Eigenvector centrality 0.000 0.588 0.134 0.169 Eigenvector centrality [Unscaled] 0.000 0.416 0.095 0.119 Eigenvector centrality per component 0.000 0.193 0.058 0.056 Closeness centrality 0.012 0.019 0.014 0.002 Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 In-Closeness centrality 0.012 0.019 0.014 0.003 In-Closeness centrality [Unscaled] 0.000 0.000 0.000 0.000 Betweenness centrality 0.000 0.056 0.006 0.011 Betweenness centrality [Unscaled] 0.000 96.667 11.050 19.732 Hub centrality 0.000 1.414 0.033 0.213 Authority centrality 0.000 1.069 0.062 0.207 Information centrality 0.000 0.068 0.023 0.021 Information centrality [Unscaled] 0.000 1.637 0.560 0.497 Clique membership count 0.000 2.000 0.279 0.542 Simmelian ties 0.000 0.000 0.000 0.000 Simmelian ties [Unscaled] 0.000 0.000 0.000 0.000 Clustering coefficient 0.000 0.500 0.061 0.138
Key Nodes
This chart shows the Task that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Task was ranked in the top three.
Total degree centrality
The Total Degree Centrality of a node is the normalized sum of its row and column degrees. Individuals or organizations who are "in the know" are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are "in the know" are identified by degree centrality in the relevant social network. Those who are ranked high on this metrics have more connections to others in the same network. The scientific name of this measure is total degree centrality and it is calculated on the agent by agent matrices.
Input network: Task x Task (size: 43, density: 0.0221484)
Rank Task Value Unscaled Context* 1 bomb_preparation 0.048 8.000 1.135 2 bombing 0.048 8.000 1.135 3 get_money 0.036 6.000 0.604 4 accusation 0.024 4.000 0.074 5 indictment 0.024 4.000 0.074 6 driving 0.024 4.000 0.074 7 weapon_training 0.018 3.000 -0.191 8 convicted 0.018 3.000 -0.191 9 conceal_bomb_in_car 0.018 3.000 -0.191 10 leave_bomb_and_car 0.018 3.000 -0.191 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.012 Mean in random network: 0.022 Std.dev: 0.011 Std.dev in random network: 0.022 In-degree centrality
The In Degree Centrality of a node is its normalized in-degree. For any node, e.g. an individual or a resource, the in-links are the connections that the node of interest receives from other nodes. For example, imagine an agent by knowledge matrix then the number of in-links a piece of knowledge has is the number of agents that are connected to. The scientific name of this measure is in-degree and it is calculated on the agent by agent matrices.
Input network(s): Task x Task
Rank Task Value Unscaled 1 bomb_preparation 0.060 5.000 2 bombing 0.048 4.000 3 indictment 0.036 3.000 4 driving 0.036 3.000 5 murder 0.024 2.000 6 destruction 0.024 2.000 7 leave_bomb_and_car 0.024 2.000 8 purchase_vehicle 0.024 2.000 9 weapon_training 0.012 1.000 10 arrest 0.012 1.000 Out-degree centrality
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest sends to other nodes. For example, imagine an agent by knowledge matrix then the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it is calculated on the agent by agent matrices. Individuals or organizations who are high in most knowledge have more expertise or are associated with more types of knowledge than are others. If no sub-network connecting agents to knowledge exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by knowledge matrices. Individuals or organizations who are high in "most resources" have more resources or are associated with more types of resources than are others. If no sub-network connecting agents to resources exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by resource matrices.
Input network(s): Task x Task
Rank Task Value Unscaled 1 get_money 0.060 5.000 2 bombing 0.048 4.000 3 bomb_preparation 0.036 3.000 4 accusation 0.036 3.000 5 weapon_training 0.024 2.000 6 driving_training 0.024 2.000 7 convicted 0.024 2.000 8 conceal_bomb_in_car 0.024 2.000 9 explosion 0.024 2.000 10 surveillence 0.012 1.000 Eigenvector centrality
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Leaders of strong cliques are individuals who or organizations who are collected to others that are themselves highly connected to each other. In other words, if you have a clique then the individual most connected to others in the clique and other cliques, is the leader of the clique. Individuals or organizations who are connected to many otherwise isolated individuals or organizations will have a much lower score in this measure then those that are connected to groups that have many connections themselves. The scientific name of this measure is eigenvector centrality and it is calculated on agent by agent matrices.
Input network: Task x Task (size: 43, density: 0.0221484)
Rank Task Value Unscaled Context* 1 bombing 0.588 0.416 -1.585 2 bomb_preparation 0.537 0.380 -1.730 3 get_money 0.474 0.335 -1.908 4 purchase_vehicle 0.373 0.264 -2.194 5 driving 0.355 0.251 -2.245 6 conceal_bomb_in_car 0.332 0.235 -2.312 7 explosion 0.317 0.224 -2.355 8 purchase_oxygen 0.289 0.205 -2.432 9 purchase_acetylene 0.289 0.205 -2.432 10 driving_training 0.270 0.191 -2.487 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.134 Mean in random network: 1.147 Std.dev: 0.169 Std.dev in random network: 0.353 Eigenvector centrality per component
Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Each component is extracted as a separate network, Eigenvector Centrality is computed on it and scaled according to the component size. The scores are then combined into a single result vector.
Input network(s): Task x Task
Rank Task Value 1 bombing 0.193 2 bomb_preparation 0.177 3 get_money 0.156 4 indictment 0.154 5 accusation 0.153 6 purchase_vehicle 0.123 7 driving 0.117 8 conceal_bomb_in_car 0.109 9 explosion 0.104 10 arrest 0.101 Closeness centrality
The average closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the average distance in the network from the node to all other nodes.
Input network: Task x Task (size: 43, density: 0.0221484)
Rank Task Value Unscaled Context* 1 provide_money 0.019 0.000 88.896 2 get_money 0.018 0.000 88.737 3 rent_residence 0.016 0.000 88.076 4 driving_training 0.016 0.000 87.960 5 run_bomb_factory 0.016 0.000 87.954 6 purchase_oxygen 0.016 0.000 87.954 7 purchase_acetylene 0.016 0.000 87.954 8 surveillence 0.016 0.000 87.949 9 purchase_vehicle 0.015 0.000 87.922 10 bomb_preparation 0.015 0.000 87.837 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.014 Mean in random network: -0.301 Std.dev: 0.002 Std.dev in random network: 0.004 In-Closeness centrality
The average closeness of a node from the other nodes in a network. Loosely, Closeness is the inverse of the average distance in the network to the node and from all other nodes.
Input network(s): Task x Task
Rank Task Value Unscaled 1 murder 0.019 0.000 2 destruction 0.019 0.000 3 explosion 0.018 0.000 4 assassination 0.018 0.000 5 bomb_preparation 0.018 0.000 6 bombing 0.018 0.000 7 driving 0.018 0.000 8 leave_bomb_and_car 0.018 0.000 9 conceal_bomb_in_car 0.018 0.000 10 weapon_training 0.018 0.000 Betweenness centrality
The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.
Input network: Task x Task (size: 43, density: 0.0221484)
Rank Task Value Unscaled Context* 1 bomb_preparation 0.056 96.667 -0.146 2 bombing 0.045 78.000 -0.257 3 leave_bomb_and_car 0.021 37.000 -0.502 4 detonate_bomb 0.019 32.000 -0.531 5 conceal_bomb_in_car 0.017 29.667 -0.545 6 trial 0.015 25.000 -0.573 7 indictment 0.014 24.000 -0.579 8 convicted 0.014 24.000 -0.579 9 accusation 0.009 16.000 -0.627 10 get_money 0.009 16.000 -0.627 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.006 Mean in random network: 0.070 Std.dev: 0.011 Std.dev in random network: 0.097 Hub centrality
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
Input network(s): Task x Task
Rank Task Value 1 get_money 1.414 2 bombing 0.000 3 weapon_training 0.000 4 explosion 0.000 5 run_bomb_factory 0.000 6 purchase_oxygen 0.000 7 purchase_acetylene 0.000 8 accusation 0.000 9 arrest 0.000 10 bomb_preparation 0.000 Authority centrality
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others. Technically, an agent is authority-central if its in-links are from agents that have are sending links to many others. The scientific name of this measure is authority centrality and it is calculated on agent by agent matrices.
Input network(s): Task x Task
Rank Task Value 1 purchase_vehicle 1.069 2 rent_residence 0.535 3 purchase_oxygen 0.535 4 purchase_acetylene 0.535 5 bomb_preparation 0.000 6 murder 0.000 7 destruction 0.000 8 explosion 0.000 9 assassination 0.000 10 indictment 0.000 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): Task x Task
Rank Task Value Unscaled 1 get_money 0.068 1.637 2 bombing 0.060 1.446 3 accusation 0.058 1.395 4 bomb_preparation 0.052 1.257 5 conceal_bomb_in_car 0.048 1.154 6 convicted 0.048 1.148 7 driving_training 0.045 1.093 8 explosion 0.045 1.091 9 weapon_training 0.042 1.000 10 driving 0.034 0.826 Clique membership count
The number of distinct cliques to which each node belongs. Individuals or organizations who are high in number of cliques are those that belong to a large number of distinct cliques. A clique is defined as a group of three or more actors that have many connections to each other and relatively fewer connections to those in other groups. The scientific name of this measure is clique count and it is calculated on the agent by agent matrices.
Input network(s): Task x Task
Rank Task Value 1 bombing 2.000 2 explosion 2.000 3 arrest 1.000 4 accusation 1.000 5 indictment 1.000 6 murder 1.000 7 destruction 1.000 8 driving 1.000 9 conceal_bomb_in_car 1.000 10 leave_bomb_and_car 1.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): Task x Task
Rank Task Value Unscaled 1 All nodes have this value 0.000 Clustering coefficient
Measures the degree of clustering in a network by averaging the clustering coefficient of each node, which is defined as the density of the node's ego network.
Input network(s): Task x Task
Rank Task Value 1 arrest 0.500 2 murder 0.500 3 destruction 0.500 4 explosion 0.333 5 accusation 0.167 6 indictment 0.167 7 conceal_bomb_in_car 0.167 8 leave_bomb_and_car 0.167 9 driving 0.083 10 bombing 0.048
Key Nodes Table
This shows the top scoring nodes side-by-side for selected measures.
Rank Betweenness centrality Closeness centrality Eigenvector centrality Eigenvector centrality per component In-degree centrality In-Closeness centrality Out-degree centrality Total degree centrality 1 bomb_preparation provide_money bombing bombing bomb_preparation murder get_money bomb_preparation 2 bombing get_money bomb_preparation bomb_preparation bombing destruction bombing bombing 3 leave_bomb_and_car rent_residence get_money get_money indictment explosion bomb_preparation get_money 4 detonate_bomb driving_training purchase_vehicle indictment driving assassination accusation accusation 5 conceal_bomb_in_car run_bomb_factory driving accusation murder bomb_preparation weapon_training indictment 6 trial purchase_oxygen conceal_bomb_in_car purchase_vehicle destruction bombing driving_training driving 7 indictment purchase_acetylene explosion driving leave_bomb_and_car driving convicted weapon_training 8 convicted surveillence purchase_oxygen conceal_bomb_in_car purchase_vehicle leave_bomb_and_car conceal_bomb_in_car convicted 9 accusation purchase_vehicle purchase_acetylene explosion weapon_training conceal_bomb_in_car explosion conceal_bomb_in_car 10 get_money bomb_preparation driving_training arrest arrest weapon_training surveillence leave_bomb_and_car